How AI Powers Ford Motorsports To The Checkered Flag

In a sport of millimeters, where the slightest design adjustment could make all the difference in a photo finish, you wouldn’t expect a car with a three-year-old body to be much of a contender.

With the help of AI, however, you can teach old cars new tricks.

Ford is optimizing its NASCAR design with the help of NVIDIA DGX.

Ford Motor Co.

Ford Motorsports has been moving up the NASCAR manufacturer rankings, from the third overall number of wins in 2016, to second in 2017. And armed with new simulation technology, the manufacturer is in pole position for the 2018 season. On June 10, Ford vehicles nabbed six of the top eight spots at the FireKeepers Casino 400, with Ford drivers finishing first, second, and third. The victory extends the momentum from the beginning of the season, when Ford vehicles took four of the top 10 spots at the season-opening Daytona 500 race on February 18, then went on to place seven cars in the top 10 at the Folds of Honor QuikTrip 500 the following week -- all with a body style that has not been significantly updated in the past three years.

But Ford does have an advantage that, according to Bryan Goodman, an engineer at Ford Motor Company, lies in the company’s new Computational Fluid Dynamics (CFD) methods, powered by the NVIDIA DGX-1 artificial intelligence (AI) supercomputer. Automakers use CFD to simulate wind tunnels, testing performance aspects of the car before a race. The simulation can tell how a car’s size and features will perform in various situations, from different track designs to driving with other vehicles. With that information, Ford can optimize the spoilers, flares, traction and other features of the vehicle for each individual racing environment. Traditionally, Goodman said, this process takes about three weeks, making it impossible to make adjustments for each race on a weekly schedule.

However, NVIDIA DGX-1 has provided Ford with 300x performance improvements, allowing Goodman’s team to speed up a three-week process to a matter of hours. With the added compute power, Ford’s deep learning team can test a wide range of situations simultaneously, rather than running one at a time and making adjustments after each simulation. Like a basketball team adjusting their entire defense after watching a few hours of tape, Ford can use hours of simulation to make wide-ranging improvements that shave critical time off the clock.